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Gemma 2B Instruct by core-outline

 »  All LLMs  »  core-outline  »  Gemma 2B Instruct   URL Share it on

  132   
  Arxiv:1705.03551   Arxiv:1804.06876   Arxiv:1804.09301   Arxiv:1809.02789   Arxiv:1811.00937   Arxiv:1904.09728   Arxiv:1905.07830   Arxiv:1905.10044   Arxiv:1907.10641   Arxiv:1911.01547   Arxiv:1911.11641   Arxiv:2009.03300   Arxiv:2009.11462   Arxiv:2101.11718   Arxiv:2107.03374   Arxiv:2108.07732   Arxiv:2109.07958   Arxiv:2110.08193   Arxiv:2110.14168   Arxiv:2203.09509   Arxiv:2206.04615   Arxiv:2304.06364   Arxiv:2312.11805   Autotrain compatible   Conversational   Endpoints compatible   Gemma   Gguf   Instruct   Quantized   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/core-outline/gemma-2b-instruct 

Gemma 2B Instruct Benchmarks

LLME Score: 0.14174
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Gemma 2B Instruct (core-outline/gemma-2b-instruct)
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Gemma 2B Instruct Parameters and Internals

Model Type 
text-to-text, decoder-only, large language model
Use Cases 
Primary Use Cases:
Content Creation and Communication - Text Generation, Chatbots and Conversational AI, Research and Education
Limitations:
Training Data Influences - Biases or gaps in training data affect responses, Context and Task Complexity - Challenging for open-ended tasks, Language Ambiguity - Struggles with nuances and figurative language, Factual Accuracy - May generate incorrect or outdated information, Common Sense - Lacks common sense reasoning
Considerations:
Developers advised to use responsibly
Additional Notes 
Benefits include high-performance open model implementations for Responsible AI development.
Supported Languages 
English (available)
Training Details 
Data Sources:
Web Documents, Code, Mathematics
Data Volume:
6 trillion tokens
Hardware Used:
TPUv5e
Safety Evaluation 
Methodologies:
Red-teaming, Ethics and safety benchmarks
Risk Categories:
Text-to-Text Content Safety, Text-to-Text Representational Harms, Memorization, Large-scale harm
Responsible Ai Considerations 
Fairness:
Models underwent careful scrutiny, input data pre-processing described and posterior evaluations reported.
Transparency:
Model card summarizes details on models' capabilities, limitations, and evaluation.
Accountability:
Encouraged to perform continuous monitoring and report misuse.
Mitigation Strategies:
Content safety guidelines; Monitoring and de-biasing techniques recommended.
Input Output 
Input Format:
Text string (e.g., question, prompt, document).
Output Format:
Generated English-language text.
LLM NameGemma 2B Instruct
Repository 🤗https://huggingface.co/core-outline/gemma-2b-instruct 
Base Model(s)  AraGemma2B Instruct   HeshamHaroon/araGemma2B-instruct
Model Size2b
Required VRAM5.1 GB
Updated2025-09-23
Maintainercore-outline
Model Typegemma
Instruction-BasedYes
Model Files  10.0 GB   5.0 GB: 1-of-2   0.1 GB: 2-of-2
GGUF QuantizationYes
Quantization Typegguf
Model ArchitectureGemmaForCausalLM
Licenseother
Context Length8192
Model Max Length8192
Transformers Version4.38.0.dev0
Tokenizer ClassGemmaTokenizer
Padding Token<pad>
Vocabulary Size256000
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than core-outline/gemma-2b-instruct.

Rank the Gemma 2B Instruct Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

Instruction Following and Task Automation  
Factuality and Completeness of Knowledge  
Censorship and Alignment  
Data Analysis and Insight Generation  
Text Generation  
Text Summarization and Feature Extraction  
Code Generation  
Multi-Language Support and Translation  

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Release v20241124
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